Green　space　accessibility　affects　housing　prices,　which　is　an　issue　that　has　been　underappreciated.　This　paper　takes　Beijing＇s　central　urban　area　as　an　example.　First,　the　Gaussian-based　two-step　floating　catchment　area　(G2SFCA)　method　is　used　to　measure　four　levels　(community　green　space,　subdistrict　green　space,　district　green　space,　and　municipal　green　space)　of　green　space　accessibility　by　multiple　travel　modes　(walking,　cycling　and　driving).　Second,　the　spatial　error　model　(SEM)　is　used　to　examine　the　factors　influencing　housing　prices.　Finally,　the　geographically　weighted　regression　(GWR)　model　is　introduced　to　explore　the　spatial　relationship　between　green　space　accessibility　by　walking,　cycling　and　driving　and　housing　prices.　The　results　show　the　following:　(1)　The　SEM　shows　that　only　driving　accessibility　has　a　positive　spatial　spillover　effect　on　housing　prices,　indicating　that　the　spatial　inequity　of　local　community　green　spaces　by　driving　will　exacerbate　surrounding　spatial　inequity.　(2)　The　GWR　model　shows　that　from　the　perspective　of　walking　and　driving　accessibility,　most　areas　within　the　Second　Ring　Road　show　spatial　equity　in　green　spaces,　while　cycling　accessibility　shows　that　the　spatial　inequity　of　green　space　exists　only　outside　the　Second　Ring　Road.　(3)　We　find　that　municipal　green　space　and　subdistrict　green　space　most　affect　the　spatial　equity　of　green　spaces.　Community　green　space,　which　is　not　open　to　the　public,　plays　a　minimal　role.　Regarding　walking　and　driving　accessibility,　municipal　green　space　increases　the　spatial　equity　of　green　spaces.　In　contrast,　this　type　of　green　space　can　promote　the　spatial　equity　of　green　spaces　regarding　cycling　accessibility.　This　study　can　significantly　improve　the　quality　of　urban　settlements　in　the　Global　South　and　has　important　implications　for　policy-makers,　planners,　and　stakeholders　in　other　large　cities　worldwide　to　create　green　and　liveable　cities.